Artificial intelligence: A powerful paradigm for scientific research
Turning Sensor Data Into Actionable Intelligence In The Era Of AI
CEO of InfluxData, a leading time series platform, board member for One Heart Worldwide and board advisor for Lucidworks and The Fabric.
gettyEverything around us is getting smarter.
Sensors have become ubiquitous in our daily lives. They cover our cars and factory floors. They're present throughout smart homes and smart cities. Sensors make the world around us smarter and more connected every day, and the language they speak—time series data—holds the key to fueling AI-driven innovation.
Real-world AI, or applying AI to the physical world, requires transforming data into valuable intelligence, making sensor-produced raw data foundational for intelligent, autonomous systems. Whether it's alerting a car when there's an object on the road, notifying a plant supervisor that a machine may overheat or letting us know when it's time to stand up and take a walk, the process of real-time data collection, transformation and response at its core is building intelligence.
Transforming time series data into intelligence is table stakes for creating and leveraging AI models, but it's also a complex process. If you can harness this data at scale, you can create intelligent, self-healing systems that continuously become smarter over time.
Instrumentation: The Cornerstone AI SystemsAny device connected to the internet produces a constant stream of data. AI algorithms leverage this data to analyze historical patterns, model behaviors and even make predictions. That's what the world is trying to do with AI—build intelligence through automated data collection so systems can predict outcomes, react to those outcomes and resolve them.
On a larger scale, if we can build intelligence around each sensor and extract insights in near real time with increasing precision, we can create intelligent—and eventually—autonomous systems.
Pulling this off requires high-resolution data—sometimes down to nanosecond precision—for real-time analytics. While not all systems require this level of granularity, having it offers benefits and enables users to find new applications for it over time.
Instrumenting systems and handling the large volume of data they create presents a challenge, however. Many organizations use analytics tools alongside their databases to visualize data according to their unique business use cases. When used effectively, this combination promotes the development of highly intelligent systems and offers opportunities for predictive analytics, forecasting and other types of real-time analysis.
Managing The Data That Powers AIA well-known adage in tech is that AI is only as strong as the data that powers it. While connected devices and software produce large volumes of highly granular data that strengthens AI systems, managing all that data creates several challenges, including:
• Managing Cardinality: When it comes to time series data, cardinality refers to a metric that shows a high number of unique or distinct values over time. Think of a sensor that measures 40 separate data points every millisecond; that sensor produces high-cardinality data that grows exponentially every minute. The trend in addressing the challenge of managing high-cardinality data is utilizing a columnar database, which supports near-real-time querying while reducing the amount of disk space necessary to store that data. Columnar databases manage data differently than row-based, relational databases, but the underlying technology should be familiar to most developers. Users need to understand the characteristics of data workloads to optimize and improve their data processing.
• Transforming And Evicting Data: The large amount of data that sensors produce can be cost-prohibitive to store, so organizations need a strategy for handling older data. The first step is transforming the data. Returning to the example of a sensor that produces 40 separate data points each millisecond, that level of granularity likely isn't necessary a few months down the road. Instead, an organization might summarize second-by-second analysis (rather than every millisecond) and then evict the rest to keep storage costs down.
• Compressing Stored Data: After transforming it, organizations still have a large amount of time series data on their hands. Shifting to columnar storage can allow for better data compression ratios, reduced file size on disk and better query performance. By aligning the data's on-disk representation with its in-memory counterpart, moving data between disk and RAM is more efficient, allowing consistent query performance while reducing costs.
To unlock the full potential of sensor data, data platforms can provide a scalable, reliable and secure environment for storing, managing and analyzing the volume, variety and velocity of IoT and sensor data. These platforms must support data processing in real time to enable businesses to build and deploy AI models that can anticipate future outcomes.
It's also important to understand the topology of your system. Where is the data coming from and where does it need to go? What are the various layers involved and where are they located? Many real-world AI applications combine edge devices and cloud-based platforms. Organizations need to understand the resources and limitations of their edge devices and optimize their performance and connectivity with costs.
Having tools that can collect data from a diverse range of sources and process it into a standardized format factors into system efficiency. Tech stacks may benefit from open-source tools and technologies that make it easier to integrate with virtually any other technology, providing greater control over data and extending its utility into new areas with less effort than what's required for proprietary solutions.
Turning Intelligence Into Insights: A Continuous EffortA significant challenge you can't overlook is the fact that turning data into intelligence is a continuous process. It's not like performing a one-time data transformation—time series data is produced continuously and never stops. It must constantly be refined, validated and transformed into the intelligence that powers real-world AI systems.
As data evolves and new data is introduced, AI models must be updated to remain current and relevant. Models must constantly adapt to new scenarios; continuous monitoring can ensure these situations are handled as they arise. Further, it's important to regularly analyze AI model performance to verify it's working correctly, particularly as new data is introduced.
As we move toward the sensor-driven future, our ability to anticipate future outcomes through AI will be a game-changer. It will enable businesses to make data-driven decisions, reduce risks, enhance customer experiences and optimize operations. In this era of sensor-driven insights, prioritizing time series data strategies and AI can help businesses lead the way, transform industries and shape the future.
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PhD Handbook : Intelligent Systems
PurposeWithin 3-4 months of full-time postgraduate study (10 months part-time), PhD candidates are expected to have a good understanding of their research area. They should also have some initial ideas about how their work might contribute to the field. The Initial Assessment is designed to check if they have reached this level of understanding.
Postgraduate researchers will be informed by the research administration about the dates for their Initial Assessment. These assessments usually occur in the first week of July or mid-January.
ContentThe assessment process involves both a written component and an oral presentation, which should be prepared in collaboration with the project supervisors.
The PhD candidate is required to deliver an oral presentation, employing suitable materials such as a PowerPoint presentation aimed at an audience comprising fellow PhD researchers, academic staff, the candidate's supervisors, and a formal Assessment Panel.
The presentation is limited to a maximum of 10 minutes, followed by a 15-minute question-and-answer session. The content of the presentation should include:
The associated written element should be presented as a concise Research Plan and must be produced in font Calibri 12 pt. Justified with 1½ line spacing and a margin of 2.54 cm.
The report will consist of a title page (to include an abstract of 300 words maximum) followed by the main text to be a maximum of four A4 pages in length, followed by a bibliography with any relevant additional information contained in appendices, e.G. A Gantt chart as presented in the oral element. References should be in IEEE format. Documents not conforming to these guidelines will be returned for adjustment.
The researcher is responsible for initiating this process using PhD Manager. Please note that the Research Integrity course must be completed on Blackboard Learn via the Portal before proceeding with the upload of the following documents:
This process should be completed at least two weeks before the date of the assessment presentation. A guide can be found on PhD Manager.
The research administrator will be responsible for making the necessary arrangements for the assessment presentation and PhD Manager will circulate the necessary documents to the Assessment Panel.
At least one member of the supervisory team is required to attend the PhD researcher's assessment session.
ProcedureThe Assessment Panel will normally consist of:
In carrying out the assessment, the reviewers will seek to answer these core questions:
The chair will be responsible for completing the necessary paperwork on PhD Manager detailing the outcome of the viva, i.E. A report on their assessment of the Research Plan, including relevant observations and/or suggestions for improvement. The Assessment Panel are empowered to recommend changes to the programme of work and the subsequent re-assessment of progress after an agreed period.
Upon receipt of the completed forms and Research Plan assessment report from PhD Manager, the panel will then confirm the outcome of the assessment to the researcher and supervisors.
See the Research Studies Guide for further information.
PurposeWithin the first year of study, at approximately month 10 for full-time(month 20 for part-time) PhD researchers shall apply to Senate for confirmation of their registration status. As a result of this assessment, the PhD researcher will either have their PhD registration status confirmed or will be invited to transfer registration and continue studying for the degree of MPhil.
ContentThe assessment comprises a written and oral element and should be prepared in collaboration with the project supervisors.
The written element comprises three components:
The report will consist of a title page (to include an abstract of 300 words maximum) followed by the main text to be a maximum of 30 A4 pages in length, followed by a bibliography with any relevant additional information contained in appendices. References should be in IEEE format.
Documents not conforming to these guidelines will be returned for adjustment.
The Literature Review should:
* Identify the specific problem area of the PhD researcher's study.* Define the problem being addressed* Summarise key existing research in this area* Conclude with a concise evaluation of previous work (possibly in tabular form) highlighting strengths, weaknesses, and knowledge gaps that the research aims to fill
The researcher is responsible for initiating this process using PhD Manager by uploading the following documents:
This process should be completed at least two weeks before the date of the assessment presentation. A guide can be found in PhD Manager.
The PhD researcher is expected to give an oral presentation to the Assessment Panel, using appropriate materials, e.G. PowerPoint, summarising the information contained in the written components. The following points are suggested as guidelines (not requirements) for the presentation content:
* Title slide.* Aims of the research and planned contribution to knowledge.* Main points of the research so far, with a plan of activity for the remaining period* Thesis outline, emphasising its logical structure and how it meets the aims of the research.* Conferences/journal publications/presentations to date or in progress.* Summary slide.
The presentation should last for a maximum of 15 minutes and is followed by 20-25 minutes for the Panel to question the PhD researcher.
The research administrator will be responsible for making the necessary arrangements for the assessment presentation and PhD Manager will circulate the written report to the Assessment Panel.
At least one member of the supervisory team is required to attend the PhD researcher's assessment session.
ProcedureThe Assessment Panel will normally consist of:
In carrying out the assessment, the reviewers will seek to answer these core questions:
The Assessment Panel will provide a report on the PhD researcher's progress and make recommendations concerning their advancement and suitability for confirmation of registration status. The Panel may make recommendations concerning the direction of the work and is empowered to recommend re-assessment after an agreed period if the case for confirmation of registration status has not yet been established.
See the Research Studies Guide for further information.
PurposeTo help prepare for final submission and examination, your Unit of Assessment (UoA) administrators will arrange for a final assessment of progress within 30 months (full-time) or 60 months (part-time) of initial registration.
This takes the form of a submission to the Festival of PhD Research, which the Doctoral College runs annually, and which showcases and celebrates research excellence at Ulster.
ContentThe assessment comprises an abstract submission to the Festival and should be organised in close cooperation with the project supervisors.
The PhD researcher is expected to give an oral presentation during the Festival using materials appropriate to the Festival audience.
The talk will last from 10-15 minutes with the opportunity for short questions from the audience.
ProcedureThe PhD researcher is responsible for submitting a draft of the intended submission to project supervisors at least 2 weeks before the final submission date of the Festival to allow time for feedback.
In advance of the assessment, the PhD researcher will be responsible for initiating the process using PhD Manager. The PhD researcher should complete the necessary details which will then automatically pass the final version of the submission to the supervisors in advance of the Festival.
After the Festival the PhD researcher will be required to create a short report (2000 words, 3000 max.) including the abstract submitted and an outline of the feedback from judges and delegates at the Festival on their research and presentation, i.E. A summary of general opinions and comments and your experience of the Festival.
The report will also include a status update on the thesis, and an outline of plans for thesis writeup (including chapter completion dates, supervisor chapter review schedule and target thesis submission dates). Once completed (within 2 weeks of the Festival date), the report must be uploaded to PhD Manager.
The report must be produced in font Calibri 12 pt. Justified with 1½ line spacing and a margin of 2.54 cm. The report will consist of a title page (to include an abstract of 300 words maximum) followed by the main text to be 3000 words max. In length, followed by a bibliography with any relevant additional information contained in appendices. References should be in IEEE format.
Documents not conforming to these guidelines will be returned for adjustment.
It's Debatable: Should The Federal Government Regulate Artificial Intelligence?
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